1、Flink Application Mode
Application Mode将在YARN上启动一个Flink集群，其中Application jar的main（）方法将在YARN中的JobManager上执行。应用程序完成后，群集将立即关闭。您可以使用yarn application-kill或取消Flink作业来手动停止集群。
由于所需的Flink jar和应用程序jar将由指定的远程位置接收，而不是由客户机发送到集群，因此上面的内容将允许作业提交变得格外轻量级。这样的话，我们就可以通过java 来进行应用程序的提交，针对于数据平台来讲比较友好。当然没有这个模式我们也可以提交，但是这种操作更加的简单便捷。
2、Submit Flink Application to Yarn
我们为什么要这样做的，在数据平台中，由于我司是2B业务，我们有一个实时数据处理平台，拖拉拽生成flink任务，那么这个时候部署的时候经常会有问题，例如交付的能力参差不齐，经常会出现一些意想不到的问题，还有环境有些不一致。这一些经常有一些问题。那么我们可以自己控制部署吗，例如哪些配置，让他们通过我们实时平台处理程序进行部署，这样也就不需要依赖flink client了，并且我们还可以在程序内部进行管控配置。极大的简化了交付工程师部署的辛苦。
当然最重要的是我们可以通过java springboot 就可以提交程序到yarn上面的，对使用者很友好，毕竟我们是拖拉拽的平台，必须要实现这个，不然拖拉拽完成了 给用户个jar包？让用户去上传jar包，这个根本不现实 不是吗。当然没有 这个flink 1.11新出的新模式，我们也实现了。我们现在急需的是配置的优化以及过程的优化。以我公司为例，阐述了一下，开发背景，接下来给大家讲解一下，怎么做呢？
2.1 配置文件准备
**1、将下载下来的flink 1.11.0版本以上的 包解压 我们需要其中的 conf目录和lib目录 **
2、将其中的conf放到IDEA中的resources中
3、将core-site.xml、hdfs-site.xml、yarn-site.xml放到resources下（因为是demo，所以我们就直接放了，并没有自己去获取）
2.2 lib 包上传到hdfs
其中标红的，去maven自己找吧。
2.3 编写 WordCount
package com.bigdata;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import java.util.Random;
import java.util.concurrent.TimeUnit;
/**
* @author ：qingzhi.wu
* @date ：2021/5/12 3:38 下午
*/
public class WordCount {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStreamSource<String> source = env.addSource(new SourceFunction<String>() {
@Override
public void run(SourceContext<String> ctx) throws Exception {
String[] words = {"spark", "flink", "hadoop", "hdfs", "yarn"};
Random random = new Random();
while (true) {
ctx.collect(words[random.nextInt(words.length)]);
TimeUnit.SECONDS.sleep(1);
}
}
@Override
public void cancel() {
}
});
source.print();
env.execute();
}
}2.4 打包上传到 hdfs上
2.5 SubmitJob
2.5.2 POM
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>org.example</groupId>
<artifactId>test_flink_mode</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<flink.version>1.11.2</flink.version>
<java.version>1.8</java.version>
<scala.binary.version>2.11</scala.binary.version>
<maven.compiler.source>${java.version}</maven.compiler.source>
<maven.compiler.target>${java.version}</maven.compiler.target>
<hadoop.version>2.7.7</hadoop.version>
</properties>
<repositories>
<repository>
<id>apache.snapshots</id>
<name>Apache Development Snapshot Repository</name>
<url>https://repository.apache.org/content/repositories/snapshots/</url>
<releases>
<enabled>false</enabled>
</releases>
<snapshots>
<enabled>true</enabled>
</snapshots>
</repository>
</repositories>
<dependencies>
<!-- Apache Flink dependencies -->
<!-- These dependencies are provided, because they should not be packaged into the JAR file. -->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-yarn_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<exclusions>
<exclusion>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-yarn-common</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
<version>1.7.7</version>
</dependency>
<dependency>
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
<version>1.2.17</version>
</dependency>
<!-- Add logging framework, to produce console output when running in the IDE. -->
<!-- These dependencies are excluded from the application JAR by default. -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-yarn-client</artifactId>
<version>${hadoop.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>${hadoop.version}</version>
<!--<scope>provided</scope>-->
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>${hadoop.version}</version>
<!-- <scope>provided</scope>-->
</dependency>
<dependency>
<groupId>commons-cli</groupId>
<artifactId>commons-cli</artifactId>
<version>1.3.1</version>
</dependency>
</dependencies>
<build>
<plugins>
<!-- Java Compiler -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.1</version>
<configuration>
<source>${java.version}</source>
<target>${java.version}</target>
</configuration>
</plugin>
<!-- We use the maven-shade plugin to create a fat jar that contains all necessary dependencies. -->
<!-- Change the value of <mainClass>...</mainClass> if your program entry point changes. -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>3.1.1</version>
<executions>
<!-- Run shade goal on package phase -->
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<artifactSet>
<excludes>
<exclude>org.apache.flink:force-shading</exclude>
<exclude>com.google.code.findbugs:jsr305</exclude>
<exclude>org.slf4j:*</exclude>
<exclude>log4j:*</exclude>
</excludes>
</artifactSet>
<filters>
<filter>
<!-- Do not copy the signatures in the META-INF folder.
Otherwise, this might cause SecurityExceptions when using the JAR. -->
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude>
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
<transformers>
<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<mainClass>com.bigdata.WordCount</mainClass>
</transformer>
</transformers>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
<pluginManagement>
<plugins>
<!-- This improves the out-of-the-box experience in Eclipse by resolving some warnings. -->
<plugin>
<groupId>org.eclipse.m2e</groupId>
<artifactId>lifecycle-mapping</artifactId>
<version>1.0.0</version>
<configuration>
<lifecycleMappingMetadata>
<pluginExecutions>
<pluginExecution>
<pluginExecutionFilter>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<versionRange>[3.1.1,)</versionRange>
<goals>
<goal>shade</goal>
</goals>
</pluginExecutionFilter>
<action>
<ignore/>
</action>
</pluginExecution>
<pluginExecution>
<pluginExecutionFilter>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<versionRange>[3.1,)</versionRange>
<goals>
<goal>testCompile</goal>
<goal>compile</goal>
</goals>
</pluginExecutionFilter>
<action>
<ignore/>
</action>
</pluginExecution>
</pluginExecutions>
</lifecycleMappingMetadata>
</configuration>
</plugin>
</plugins>
</pluginManagement>
</build>
</project>2.5.3 APP
package com.bigdata;
import org.apache.flink.client.deployment.ClusterDeploymentException;
import org.apache.flink.client.deployment.ClusterSpecification;
import org.apache.flink.client.deployment.application.ApplicationConfiguration;
import org.apache.flink.client.program.ClusterClient;
import org.apache.flink.client.program.ClusterClientProvider;
import org.apache.flink.configuration.*;
import org.apache.flink.yarn.YarnClientYarnClusterInformationRetriever;
import org.apache.flink.yarn.YarnClusterDescriptor;
import org.apache.flink.yarn.YarnClusterInformationRetriever;
import org.apache.flink.yarn.configuration.YarnConfigOptions;
import org.apache.flink.yarn.configuration.YarnDeploymentTarget;
import org.apache.flink.yarn.configuration.YarnLogConfigUtil;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.yarn.api.records.ApplicationId;
import org.apache.hadoop.yarn.client.api.YarnClient;
import org.apache.hadoop.yarn.conf.YarnConfiguration;
import java.util.Collections;
import static org.apache.flink.configuration.MemorySize.MemoryUnit.MEGA_BYTES;
/**
* @author ：qingzhi.wu
* @date ：2021/5/12 7:16 下午
*/
public class App {
public static void main(String[] args) {
///home/root/flink/lib/lib
System.setProperty("HADOOP_USER_NAME","root");
String configurationDirectory = "/Users/wuqingzhi/java_work_space/test_flink_mode/src/main/resources/conf/";
org.apache.hadoop.conf.Configuration conf = new org.apache.hadoop.conf.Configuration();
conf.set("fs.hdfs.impl","org.apache.hadoop.hdfs.DistributedFileSystem");
String flinkLibs = "hdfs://node01:9000/data/flink/libs";
String userJarPath = "hdfs://node01:9000/data/flink/user-lib/7.jar";
String flinkDistJar = "hdfs://node01:9000/data/flink/libs/flink-yarn_2.12-1.11.2.jar";
YarnClient yarnClient = YarnClient.createYarnClient();
YarnConfiguration yarnConfiguration = new YarnConfiguration();
yarnClient.init(yarnConfiguration);
yarnClient.start();
YarnClusterInformationRetriever clusterInformationRetriever = YarnClientYarnClusterInformationRetriever
.create(yarnClient);
//获取flink的配置
Configuration flinkConfiguration = GlobalConfiguration.loadConfiguration(
configurationDirectory);
flinkConfiguration.set(CheckpointingOptions.INCREMENTAL_CHECKPOINTS, true);
flinkConfiguration.set(
PipelineOptions.JARS,
Collections.singletonList(
userJarPath));
YarnLogConfigUtil.setLogConfigFileInConfig(flinkConfiguration,configurationDirectory);
Path remoteLib = new Path(flinkLibs);
flinkConfiguration.set(
YarnConfigOptions.PROVIDED_LIB_DIRS,
Collections.singletonList(remoteLib.toString()));
flinkConfiguration.set(
YarnConfigOptions.FLINK_DIST_JAR,
flinkDistJar);
//设置为application模式
flinkConfiguration.set(
DeploymentOptions.TARGET,
YarnDeploymentTarget.APPLICATION.getName());
//yarn application name
flinkConfiguration.set(YarnConfigOptions.APPLICATION_NAME, "jobname");
//设置配置，可以设置很多
flinkConfiguration.set(JobManagerOptions.TOTAL_PROCESS_MEMORY, MemorySize.parse("1024",MEGA_BYTES));
flinkConfiguration.set(TaskManagerOptions.TOTAL_PROCESS_MEMORY, MemorySize.parse("1024",MEGA_BYTES));
ClusterSpecification clusterSpecification = new ClusterSpecification.ClusterSpecificationBuilder()
.createClusterSpecification();
// 设置用户jar的参数和主类
ApplicationConfiguration appConfig = new ApplicationConfiguration(args,"com.bigdata.WordCount");
YarnClusterDescriptor yarnClusterDescriptor = new YarnClusterDescriptor(
flinkConfiguration,
yarnConfiguration,
yarnClient,
clusterInformationRetriever,
true);
ClusterClientProvider<ApplicationId> clusterClientProvider = null;
try {
clusterClientProvider = yarnClusterDescriptor.deployApplicationCluster(
clusterSpecification,
appConfig);
} catch (ClusterDeploymentException e){
e.printStackTrace();
}
ClusterClient<ApplicationId> clusterClient = clusterClientProvider.getClusterClient();
ApplicationId applicationId = clusterClient.getClusterId();
System.out.println(applicationId);
}
}
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-YIx52fwS-1620963672506)(/Users/wuqingzhi/Library/Application Support/typora-user-images/image-20210514113805180.png)]
到此完成，其实可以进行很多优化的，但是毕竟不是生产代码，就简单的写写demo，谢谢大家的观看。
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